Convex Optimization In Identification Of Stable Non-Linear State Space Models
Optimization and Control
2016-11-17 v1 Dynamical Systems
Abstract
A new framework for nonlinear system identification is presented in terms of optimal fitting of stable nonlinear state space equations to input/output/state data, with a performance objective defined as a measure of robustness of the simulation error with respect to equation errors. Basic definitions and analytical results are presented. The utility of the method is illustrated on a simple simulation example as well as experimental recordings from a live neuron.
Cite
@article{arxiv.1009.1670,
title = {Convex Optimization In Identification Of Stable Non-Linear State Space Models},
author = {Mark M. Tobenkin and Ian R. Manchester and Jennifer Wang and Alexandre Megretski and Russ Tedrake},
journal= {arXiv preprint arXiv:1009.1670},
year = {2016}
}
Comments
9 pages, 2 figure, elaboration of same-title paper in 49th IEEE Conference on Decision and Control